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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Analysis of energy usage for HUGS models

Based on the energy_star branch of optimum-benchmark, and using codecarbon.

Fields

  • task: Task the model was benchmarked on.
  • org: Organization hosting the model.
  • model: The specific model. Model names at HF are usually constructed with {org}/{model}.
  • date: The date that the benchmark was run.
  • prefill: The esimated energy and efficiency for prefilling.
  • decode: The estimated energy and efficiency for decoding.
  • preprocess: The estimated energy and efficiency for preprocessing.

Code to Reproduce

As I'm devving, I'm hopping between https://huggingface.co/spaces/AIEnergyScore/benchmark-hugs-models and https://huggingface.co/spaces/meg/CalculateCarbon

From there, python code/make_pretty_dataset.py (included in this repository) takes the raw results and uploads them to the dataset here.

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